set_verbose » verbose
validate_environment » all fields
field frequency_penalty: float = 0#
Penalizes repeated tokens according to frequency.
field logit_bias: Optional[Dict[str, float]] [Optional]#
Adjust the probability of specific tokens being generated.
field max_tokens: int = 256#
The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size.
field min_tokens: int = 1#
The minimum number of tokens to generate in the completion.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-neo-20b'#
Model name to use
field n: int = 1#
How many completions to generate for each prompt.
field presence_penalty: float = 0#
Penalizes repeated tokens.
field temperature: float = 0.7#
What sampling temperature to use
field top_p: float = 1#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#